Title :
A powerful finite mixture model based on the generalized Dirichlet distribution: unsupervised learning and applications
Author :
Bouguila, Nizar ; Ziou, Djemel
Author_Institution :
Sherbrooke Univ., Que., Canada
Abstract :
This paper presents a new finite mixture model based on a generalization of the Dirichlet distribution. For the estimation of the parameters of this mixture we use a GEM (generalized expectation maximization) algorithm based on a Newton-Raphson step. The experimental results involve the comparison of the performance of Gaussian and generalized Dirichlet mixtures in the classification of several pattern-recognition data sets.
Keywords :
Gaussian processes; Newton-Raphson method; optimisation; pattern recognition; unsupervised learning; Gaussian mixture; Newton-Raphson method; finite mixture model; generalized Dirichlet distribution; generalized Dirichlet mixture; generalized expectation maximization algorithm; pattern recognition data sets; unsupervised learning; Character generation; Covariance matrix; Image processing; Machine learning; Machine learning algorithms; Maximum likelihood estimation; Parameter estimation; Pattern recognition; Statistical distributions; Unsupervised learning;
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Print_ISBN :
0-7695-2128-2
DOI :
10.1109/ICPR.2004.1334107
